ThotDjehuty
cce31055c1
Add scripts/inject_doc_plots.py that scans every .md and .rst page under docs/source/, executes each Python code-block in an isolated namespace with a non-interactive matplotlib backend, captures every figure produced, and inserts an inline image directive immediately after the code-block. Markers AUTO-PLOT-BEGIN/END make the injection idempotent on re-runs. Blocks that fail to execute or produce no figure are left untouched. Add a transparent __getattr__ fallback in python/optimizr/__init__.py that forwards any unresolved top-level attribute to the compiled _core extension. This lets all v1.x and v2.0 doc samples that use 'from optimizr import X' (estimate_ou_params_py, linear_bsde_constant_coeffs, mmd_gaussian, ...) execute as written. Augment the OU Parameter Estimation example (docs/source/algorithms/optimal_control.md) with a two-panel visualization (simulated path plus empirical/theoretical autocorrelation). Net effect: 14 doc pages now display matplotlib plots inline directly under the code that produced them -- including the OU page, point processes, Grid Search, HMM, MCMC, plus the 8 v2.0 RST pages.
123 lines
3.2 KiB
Python
123 lines
3.2 KiB
Python
"""
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OptimizR - High-Performance Optimization Algorithms
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===================================================
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Fast, reliable implementations of advanced optimization and statistical
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inference algorithms with Rust acceleration and pure Python fallbacks.
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.. moduleauthor:: OptimizR Contributors
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"""
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from optimizr.hmm import HMM
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from optimizr.core import (
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mcmc_sample,
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differential_evolution,
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parallel_differential_evolution_rust,
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grid_search,
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mutual_information,
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shannon_entropy,
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sparse_pca_py,
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box_tao_decomposition_py,
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elastic_net_py,
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hurst_exponent_py,
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compute_risk_metrics_py,
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estimate_half_life_py,
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bootstrap_returns_py,
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# Time-series utilities
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prepare_for_hmm_py,
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rolling_hurst_exponent_py,
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rolling_half_life_py,
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return_statistics_py,
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create_lagged_features_py,
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rolling_correlation_py,
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# Benchmark functions
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Sphere,
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Rosenbrock,
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Rastrigin,
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Ackley,
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Griewank,
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)
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# Try to import maths_toolkit and mean_field from Rust backend
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try:
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from optimizr import _core
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maths_toolkit = _core
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MFGConfig = _core.MFGConfigPy
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solve_mfg_1d_rust = _core.solve_mfg_1d_rust
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except (ImportError, AttributeError):
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maths_toolkit = None
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MFGConfig = None
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solve_mfg_1d_rust = None
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# Portfolio Optimization (CARA, Mean-Variance, ERC)
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try:
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from optimizr._core import (
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cara_optimal_weights,
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mean_variance_optimal_weights,
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min_variance_weights,
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erc_weights,
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)
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except (ImportError, AttributeError):
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cara_optimal_weights = None
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mean_variance_optimal_weights = None
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min_variance_weights = None
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erc_weights = None
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__version__ = "0.2.0"
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__all__ = [
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"HMM",
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"mcmc_sample",
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"differential_evolution",
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"parallel_differential_evolution_rust",
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"grid_search",
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"mutual_information",
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"shannon_entropy",
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"sparse_pca_py",
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"box_tao_decomposition_py",
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"elastic_net_py",
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"hurst_exponent_py",
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"compute_risk_metrics_py",
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"estimate_half_life_py",
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"bootstrap_returns_py",
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# Time-series utilities
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"prepare_for_hmm_py",
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"rolling_hurst_exponent_py",
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"rolling_half_life_py",
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"return_statistics_py",
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"create_lagged_features_py",
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"rolling_correlation_py",
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# Benchmark functions
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"Sphere",
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"Rosenbrock",
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"Rastrigin",
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"Ackley",
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"Griewank",
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"maths_toolkit",
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# Mean Field Games
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"MFGConfig",
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"solve_mfg_1d_rust",
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# Portfolio Optimization
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"cara_optimal_weights",
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"mean_variance_optimal_weights",
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"min_variance_weights",
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"erc_weights",
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]
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def __getattr__(name):
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"""Transparent fallback: forward any unresolved top-level attribute access
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to the compiled `_core` extension. This keeps `from optimizr import X`
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working for every Rust-backed function (v1.x and v2.0 primitives) without
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having to enumerate the full list above."""
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try:
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from optimizr import _core as _ext
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except ImportError as exc: # pragma: no cover
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raise AttributeError(
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f"module 'optimizr' has no attribute {name!r} "
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f"(_core extension is not built: {exc})"
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) from exc
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if hasattr(_ext, name):
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return getattr(_ext, name)
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raise AttributeError(f"module 'optimizr' has no attribute {name!r}")
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